survival via sponsorship alliances: not all exchanged resources are equal

SURVIVAL VIA SPONSORSHIP 1

APA cite: Cobbs, J. B., Tyler, B. D., & Chan, C. K. (2012, June). Survival via sponsorship alliances: Not all

exchanged resources are equal. Research presented at the Warsaw Sport Marketing Center’s Research

Conference in Sport Marketing: Focus on Sponsorship, Portland, OR.

Running head: SURVIVAL VIA SPONSORSHIP

SURVIVAL VIA SPONSORSHIP ALLIANCES: NOT ALL EXCHANGED RESOURCES

ARE EQUAL

JOE COBBS

Northern Kentucky University

Haile/US Bank College of Business

Nunn Drive

Highland Heights, KY 41099

Tel: (859) 572-7960

Fax: (859) 572-5150

e-mail: [email protected]

B. DAVID TYLER

Western Carolina University

College of Business

203 Forsyth

Cullowhee, NC 28723

Tel: (828) 283-0203

Fax: (828) 227-7414


C. KWONG CHAN

Nielsen Worldwide

188 Nanking East Road

Section 5, 12/F National Enterprise Center

105 Taipei

Taiwan

Tel: +886 2 - 27568668

Fax: +886 2 - 27645387






ABSTRACT

Many sports and cultural enterprises—such as events, teams, leagues, and facilities—

rely on interorganizational exchange to access resources. Sponsorship alliances are a common

method for resource acquisition; yet, this study reveals not all sponsorships are equal in their

contribution to enterprise survival. An event history model examining 40 years of corporate

sponsorship in Formula One motor racing demonstrates that sponsorships offering the team

financial or performance-based resources, as opposed to operational resources, significantly

reduce the probability of team dissolution (failure). However, managing corporate relationships

also entail certain costs to the team, and the impact of such sponsorships on team survival is not

immune to diminishing returns.

KEYWORDS: resource dependence, heterogeneous resources, alliance dependence,

sponsorship, organization survival, Formula One





INTRODUCTION

While the popularity of the resourced-based view (RBV) has focused attention on the

contribution of organizational resources to a sustained competitive advantage (Barney, 1991;

Wernerfelt, 1984), the mere sustainability of operations concerns many enterprises. Ulrich and

Barney (1984) acknowledged this reality when they integrated resource dependency and

population ecology theory to suggest that firms must efficiently engage in an acquisition of

resources to survive over the long term. Interorganizational alliances are one of the most

common methods for accessing the resources necessary for firm survival (Ireland, Hitt, and

Vaidyanath, 2002). However, relying on alliances for resource access entails some dilemmas

and can be detrimental over time. Organizational resources are not homogeneous (Grant, 1991),

and a firm’s alliance capacity has limits (Deeds & Hill, 1996). As a result, prioritization of

resources is necessary when structuring alliance collaboration.

The purpose of the following study is to examine the heterogeneity of alliance-based

resources by investigating the relative influence of differing resources on firm survival. To

achieve this aim, we test an event history model utilizing 40 years of data on the alliances

between 124 Formula One (F1) racing teams and 1,077 supporting corporate partners. F1 teams

access various categories of resources via their corporate sponsorship alliances in order to

enhance performance and maintain operations on an international scale. Explicitly, the

overarching research question asks how an enterprise’s access to heterogeneous resources

through interorganizational alliances influences survival of the enterprise in a highly competitive

environment.

Alliance-based Resource Exchange

Exchange theorists have pointed out that relationships between organizations enable

access to resources outside the traditional constraints of the firm (Levine & White, 1961). Often





such relationships take the form of a strategic alliance between firms (Ireland et al., 2002).

While the definitions and rationales for a strategic alliance have proliferated in the management

literature (e.g., Das & Teng, 2000; Gulati, 1999; Saxton, 1997; Varadarajan & Cunningham,

1995), two consistent elements synthesize the concept: cooperative relationships and resource

exchange. Given these commonalities, a strategic alliance is perhaps best described as a

“cooperative relationship driven by a logic of strategic resource needs and social resource

opportunities” (Eisenhardt & Schoonhoven, 1996: 137).

Strategic alliances are attractive mechanisms for accessing desired resources outside the

boundaries of the firm because partners can realize the benefits of resources without the full

commitment required of ownership. Further, alliances facilitate access to not only the core

resources offered by the partner, but also the accumulated knowledge and experience of that

partner in leveraging such a resource (Hitt, Dacin, Levitas, Arregle, & Borza, 2000).

Meanwhile, by offering their own expertise and tradable resources as part of the

interorganizational exchange inherent in an alliance, each partner potentially extracts incremental

value from their own resource inventory. Consequently, alliances are a viable means by which

organizations navigate their environment and maintain survival (Baum, Calabrese, & Silverman,

2000).

Nonetheless, in composing resource dependency theory, Pfeffer and Nowak (1976)

suggest interorganizational resource exchange is not without its drawbacks. Relying on entities

outside the firm for access to vital resources fosters dependency and initiates another set of

constraints on the firm. Interorganizational exchanges are not devoid of power dynamics, and

resource dependency implies the ceding of a degree of power to an alliance partner (Cook, 1977;

Emerson, 1962). Within the dependency perspective, scholars generally hypothesize firms to





avoid such constraints; yet in reality, the demands of adapting to a dynamic environment

typically result in a pragmatic balancing of dependency and efficiency that necessitates a certain

level of interorganizational exchange to ensure survival (Baker, 1990; Ulrich & Barney, 1984).

Furthermore, organizations do not possess an unbounded capacity for alliance engagement and

therefore, firms must structure and prioritize the resource needs and opportunities that arise

(Deeds & Hill, 1996).

Resource Heterogeneity

According to the resource-based view of the firm, resources are heterogeneous in both

their distribution and contribution to competition across firms (Barney, 1991). In other words,

organizational resources are not evenly dispersed and their utility is not equivalent. Grant (1991)

identified six general categories of resources: financial, physical, human, technological,

reputation, and organizational. In empirical contexts emphasizing resource exchange,

researchers have routinely reduced the categorization to designations such as technical versus

nontechnical (Chan, Kensinger, Keown, & Martin, 1997), marketing versus research (Anand &

Khanna, 2000), and technological versus marketing (Das, Sen, & Sengupta, 1998). However,

each of these resource categories tends to describe the actual resource and not necessarily the

resource or alliance’s potential contribution to the firm in relation to its competitive environment.

Yet certain alliance resources may offer a more direct contribution to an organization’s

competitive advantage in a particular environment.

In their empirical examination of the deployment of firm resources, Slotegraaf et al.

(2003) moved closer to a contribution-based perspective when specifying the differential

effectiveness of various resources, but their delineation as financial, technological, and

marketing resources remained descriptive of the resource itself and not necessarily its ultimate





utilization. Brush, Greene, and Hart (2001: 67) tackled the issue of resource distinction by

proposing that firm resources be “characterized by their application to the productive process,

ranging from utilitarian to instrumental.” Under such a classification, utilitarian resources were

applied directly to production, while instrumental resources were utilized in a more flexible

fashion to gain access to other resources. Meanwhile, other researchers have approached

resource application by characterizing the resource exchange within an alliance relationship

based on a comparison of each partner’s industry or operational context. This perspective is

based on complementarity (Chung, Singh, & Lee, 2000; Sarkar, Echambadi, Cavusgil, &

Aulakh, 2001), or strategic relatedness (Tsai, 2000), and implies not only that resources are

heterogeneously distributed across firms and industries, but also encompasses the former idea

that resources retain a strategic dimension relating to their potential utilization.

In this paper, we categorize diverse resources based on their potential strategic

contribution to an enterprise and by doing so, we take into account the acquiring enterprise’s

competitive and institutional context (i.e., F1 racing). Alliance partners offering resources from

highly complementary industries (e.g., automotive, high technology, aerospace) with a direct

influence on competition, we categorize as performance-based resources. Alliances providing

resources more instrumental to the running of the business and less related to direct competition

with rivals, we label as operational resources. Lastly, monetary contributions we consider as

financial resources, which offer flexibility in their utilization to acquire further necessary

resources.

Corporate Sponsorship

In the charity, arts, entertainment, and sports industries, corporate sponsorship has

flourished as a structured exchange mechanism whereby the industries’ enterprises can access





heterogeneous resources from commercial organizations in return for promotional affiliation and

enhancement (Meenaghan, 2001). This particular type of alliance offers an interesting and

relevant platform for interorganizational research for several reasons. First, scholars have

recognized the potential for a sponsorship alliance to differentiate and add financial value to a

sponsoring firm’s brand while serving as a primary method for resource acquisition for

sponsored organizations (Cliffea & Motion, 2005; Cornwell, Roy, & Steinard, 2001). For

example, since 1995, Shell has provided the Ferrari F1 racing team with diverse resources such

as financial investment, petroleum products, and technological performance expertise. In

exchange, Ferrari provides Shell with the F1 sponsorship resources to meet Shell’s promotional

objectives of creating awareness for Shell’s premium products, sustaining its perceived position

as a technology leader, solidifying key stakeholder relationships via event hospitality, and

encouraging purchase through themed point-of-sale promotions (Verity, 2002).

Second, because corporate sponsorship has become an institutionalized support

mechanism ubiquitous throughout the arts, entertainment, sports, and cultural events in modern

society, engagement in this type of alliance spans a myriad of industries, organizations,

activities, and managers (Crowley, 1991). Third, the growing popularity of sponsorship alliances

as a mechanism for resource exchange is evidenced by the rapid expansion of corporate

sponsorship investment, which reached an estimated worldwide expenditure of US$48.7 billion

in 2011—an increase of over 28 percent from the previous four years (IEG, 2011). Lastly, the

promotional nature of sponsorship alliances typically requires the organization or activity being

sponsored to have some degree of popularity or potential for publicizing the relationship. As a

result, data on alliances between sponsored enterprises and sponsoring firms can often be

obtained or verified through public sources; and in the case of sports sponsorships—which





comprise over two-thirds of all sponsorship investments (IEG, 2011)—data on competitive

organizational performance is compiled and archived religiously by numerous independent

media outlets. Given these factors and the potential contribution to the broader

interorganizational alliance context, corporate sponsorship presents a promising context for

empirical research on the question of how differing resources accessed through

interorganizational alliances impact the longevity of an enterprise engaged in intense

competition.

HYPOTHESES

The survival of an organizational enterprise offers an intriguing investigative outcome

relevant to a range of research disciplines, including economics (Audretsch & Mahmood, 1995),

psychology (Shaver & Scott, 1991), sociology (Thornton, 1999), and management theorists

(Lange, Boivie, & Henderson, 2009). Resource dependency, organizational ecology, and

organizational learning theories suggest several factors that could plausibly be considered as

influential in an enterprise’s survival. The central premise of this paper is that enterprise survival

depends, to some degree, on leveraging internal resources in the marketplace to facilitate

alliances with other firms and thereby gain access to the additional resources necessary for

success in competition with rivals. By focusing on interorganizational alliances for resource

exchange, we seek to advance research that has taken a resource-based approach to investigating

the survival of organizations (e.g., Alvarez & Busenitz, 2001; Bergmann Lichtenstein & Brush,

2001; Sheppard, 1995; Starr & MacMillan, 1990). The theoretical model detailed by the

following hypotheses stipulates that, when controlling for competitive performance, the

probability of an organization dissolving (i.e., falling out of existence) is a function of: (a) access





to performance-based, financial, and operational resources; and (b) the experience of alliance

partners in the competitive context (i.e., F1 racing).

Considering the heterogeneity of organizational resources (e.g., Grant, 1991), alliances

based on resource exchange can be theoretically classified by the role of the acquired resources

in the functioning of the acquiring enterprise (Brush et al., 2001). Complementarity with

alliance partners offers a distinctive basis for prioritization of exchanged resources. Sarkar,

Echambadli, Cavusgil, et al. (2001: 360) conceptualize alliance resource complementarity as a

symmetry consisting of “unique and valuable resources available to achieve strategic objectives,”

and thus enhance “competitive viability.” This consideration of competitiveness implies a

proper emphasis on the institutional context of resource utilization, therein suggesting that

certain resources may be more or less relevant to an enterprise’s survival based on the

competition faced in a specific environment. To that end, we propose three resource distinctions

within this study—performance-based resources, financial or monetary resources, and

operational resources—all of which are hypothesized to be negatively related to enterprise

dissolution to varying degrees based on the complementarity to the institutional context. In other

words, the better an acquired resource fits the competitive context, the more likely that resource

contributes to enterprise survival.

Performance-based Resources

The first resource designation—labeled performance-based—is signified by direct

complementarity, from a competitive perspective, between alliance partners. Accessing

complementary resources to enhance performance is a foundation of alliance formation (Chung

et al., 2000; Das & Teng, 2000), and yet, organizational performance is a function of competition

among rivals (Hannan & Freeman, 1977; Porter, 1991). Thus, an alliance partner that shares an





industry with, or operates in an industry strongly related to that of the focal enterprise is likely to

be better equipped to offer strategic resources to combat rivals by directly impacting enterprise

performance (Varadarajan & Cunningham, 1995).

Bergmann Lichtenstein and Brush (2001) found resources relating directly to the

production process to be more salient to entrepreneurs, who typically pursued such resources

through interorganizational partnerships in congruent industries. This assertion follows

organizational learning theory, which suggests entrepreneurial enterprises can compensate for

their liability of newness by gaining relevant industry knowledge through alliances with

established firms (Freeman, Carroll, & Hannan, 1983; Hamel, 1991). However, as the

entrepreneurial enterprise accumulates competitive experience over time, the necessity of

accessing performance-based resources may diminish (Bergmann Lichtenstein & Brush, 2001).

As a result, we anticipate resources exchanged within congruent industry alliances to exert a

positive influence on an enterprise’s performance in head-to-head competition and thereby stave

off organizational dissolution; but we expect the survival effect of such alliances to diminish as

an enterprise accumulates competitive experience.

Hypothesis 1. (a) An enterprise’s access to performance-based resources is most

influential among resource types in reducing the probability of enterprise dissolution.

(b) However, as an enterprise gains experience, the negative relationship between

dissolution and performance-based alliances weakens.

Financial Resources

Financial or monetary resources represent the most ubiquitous designation of firm

resource categorization (e.g., Barney, 1995; Grant, 1991; Morgan & Hunt, 1999), which is less

than surprising given their versatility. Financial resources possess not only an intrinsic





transformative nature, but can also symbolize the ultimate aim of an enterprise to many

individuals—that being to increase financial wealth or simply make money. Indeed, this money-

making condition is so vital to an entrepreneurial enterprise that the numbers test (i.e., how will

the business make money?) entails one of the two essential tenets of a viable business model

(versus the narrative test) (Magretta, 2002).

Beyond simply creating monetary wealth, financial resources provide an organization

with flexibility because their quality of liquidity enables these resources to be quickly exchanged

for another resource deemed at the time to be vital to the enterprise. Yet, Barney (1991) points

out financial resources are often not rare and are therefore unlikely to solely generate a

sustainable competitive advantage. While accessing financial resources may be imperative to

entrepreneurial survival, some research has shown that in the early stages of enterprise

development, financial resources, though relevant, are not as salient to the entrepreneur as those

resources relating directly to performance (Bergmann Lichtenstein & Brush, 2001; Brush et al.,

2001). Therefore, we hypothesize financial resources to contribute to an enterprise’s continued

existence, but not be as vital to survival as performance-based resources.

Hypothesis 2. An enterprise’s access to financial resources reduces the probability of

enterprise dissolution, but at a lower intensity than performance-based resources.

Operational Resources

Resources contributing to the ongoing operation of an enterprise, but not strictly

monetary or straightforwardly influencing an enterprise’s direct competition with rivals we label

as operational resources. This category is characterized by the commodity goods and services

necessary for the continued functioning of an organization and instrumental to accessing further

resources (Brush et al., 2001). For most enterprises, such resources might include office





equipment, internet access, certain basic employee services, and other administrative capabilities.

This type of resource is typically not the primary basis for competition within an industry and

tends to be easier to access given their near universal utilization. As a result, operational

resources are not particularly rare, and similar to financial resources, are therefore unlikely to be

a source of competitive advantage (Barney, 1991). However, these resources also lack the

flexibility and liquidity of pure financial resources. Hence, we expect operational resources to

contribute to organizational survival, but be less influential than either performance or financial-

based resources in predicting the dissolution of entrepreneurial enterprises.

Hypothesis 3. An enterprise’s access to operational resources reduces the probability of

enterprise dissolution, but is less influential than performance or financial resources.

Alliance Management Constraints

Though alliances offer a convenient mechanism for interorganizational resource

exchange, like individuals, a firm’s capacity for managing relationships may be bounded and

subject to diminishing returns (McFadyen & Cannella, 2004). Organizations possess an alliance

management capability that is not without constraints (Ireland et al., 2002). Managing different

types and increasing numbers of alliances can strain an enterprise’s capability for maximizing

returns to the numerous relationships (Rothaermel & Deeds, 2006). Deeds and Hill (1996)

uncovered this effect of diminishing returns to alliances when investigating rates of new product

development. They describe the phenomenon as arising “because the effectiveness with which

the firm can select and manage alliance partners is likely to be negatively related to the number

of alliances the firm is managing” (p. 42). Given this cost to managing increasing alliance

relationships, too many alliances may eventually exhibit diminishing returns and be detrimental

to an enterprise’s continuity.





Hypothesis 4. Increasing numbers of (a) performance-based, (b) financial, and (c)

operational alliances will exhibit diminishing returns to reducing the probability of

enterprise dissolution.

Alliance Partners’ Experience

At their inception, enterprises often must overcome what Freeman et al. (1983) term the

liability of newness. In other words, the likely survival of an organization increases with age,

which implies that experience contributes to survival. While new ventures cannot artificially

contrive their own organizational experience apart from the restrictions of time, Levitt and

March (1988) propose in organizational learning theory that the knowledge benefits of

experience can diffuse between organizations via three primary methods. First, a central

organization such as a trade association might broadcast best practices for an industry to its

community of affiliated organizations. Second, direct contact between organizations through an

alliance, personnel transfer, or interlocking directorates can diffuse routines, strategies, or other

knowledge between enterprises. Finally, experience-based information may be collected and

disseminated within a small group, such as industry consultants or trainers, which then broadcast

the knowledge to a larger organizational population.

Where an entrepreneurial enterprise looks to interorganizational alliances to access

resources, the enterprise positions itself to take advantage of the second method of knowledge

diffusion described above. As contact between the aligned organizations intensifies, routines

develop that can be conducive to knowledge accumulation and organizational learning (Zollo,

Reuer, & Singh, 2003). While both parties in an alliance may realize learning effects, the effects

are not necessarily symmetrical. The less experienced partner maintains a greater learning

potential as related to the competitive context than its more experienced partner (Hamel, 1991;





Hitt et al., 2000). Furthermore, alliance management capability can be augmented by the prior

alliance experience of a partner firm (Hoang & Rothaermel, 2005). In an entrepreneurial

situation, this relational benefit implies that new ventures would be wise to seek out alliances

with firms that have accumulated experience in the venture’s competitive context. Doing so may

result in knowledge accumulation through organizational learning that compensates for the

venture’s own lack of experience.

Hypothesis 5. The context experience of an enterprise’s alliance network is negatively

related to the probability of enterprise dissolution.

METHODS

To test the influence of alliances offering heterogeneous resources on the survival of

competing enterprises, we employed the event history methodology—commonly referred to as

survival analysis—in the context of F1 motor racing. In lamenting the lack of empirical

organizational survival research, Audretsch and Mahmood (1995) identified three measurement

issues that have traditionally impeded progress: (1) the lack of longitudinal data compilations

with discrete startup and closure information; (2) the challenge of determining observations in

close enough time intervals on a consistent basis (e.g., census data tends to be too

chronologically sparse); and, 3) data must be available at the organizational level, as opposed to

aggregated at the industry level. F1 racing teams as entrepreneurial enterprises provide several

research advantages that overcome these challenges.

First, F1 teams have a discrete point of market entry and exit that is well documented and

defined by their race participation. The modern F1 race schedule includes close to twenty races

that generate more revenue per event than any other yearly sport while reaching every continent

except Antarctica (Sylt & Reid, 2008b). Yet, unlike North American sports leagues that operate





as closed leagues where teams rarely enter and exit competition, the sporting institution of F1

allows for an annual turnover of new team entrants and existing team dissolutions.

Second, each team operates as an independent organization that must harness and utilize

resources to produce a product that publicly competes against the other products in the industry

(Collings, 2004). Numerous F1 teams operate with annual budgets in excess of US$300 million,

or ten-fold greater than the cost of fielding an entry into the NASCAR Sprint Cup Series—the

most popular US racing series (Smith, 2008). Such substantial annual budgets in F1 necessitate

a strong dependence on sponsorship alliances to underwrite team operations. While a typical F1

racing team generates over 70% of its operating budget from these corporate relationships (Sylt

& Reid, 2008a), alliance partners also contribute considerable technological, logistical, and

operational expertise (Castellucci & Ertug, 2010). Motor racing encompasses a rigorous testing

environment for both automotive and technological product development, thereby encouraging a

diverse resource exchange inclusive of both property and knowledge-based resources (Jenkins &

Floyd, 2001).

Finally, each F1 team must manufacture their core product, the race car, within the

regulations set forth by the Fédération Internationale de l'Automobile (FIA)—the global

governing body of motor sports—similar to how an construction enterprise would be subject to

building codes or a food producer must comply with governmental food regulations. Race

results objectively capture the performance of these products in competition, and the

interorganizational alliances of each team are publicly touted and praised for their contribution to

the teams’ continued survival and competitive success (Hotten, 2000). One F1 team manager

highlighted this point by stating “our commercial proposition is founded on the principle of





community and the clear expectation that our partners will enjoy being with each other and

working together for their mutual benefit, as well as ours” (Sylt & Reid, 2008b).

Sample

Given the historical international popularity of F1 racing and the characteristics described

above, data on F1 team existence and alliances with sponsoring firms was feasible to compile

from the institution’s formal organization in 1950 through 20071. We acquired the foundation of

the data from the online database ChicaneF1, which is widely recognized as the most

comprehensive source of historical team and sponsoring firm alliance information available

(Davies & Lawrence, n.d.). Next, the data were crosschecked with data we obtained from an

internal F1 team source to verify reliability (Black Book Formula One, 2007). We further

consulted historical F1 texts containing pictures of various teams’ race cars in an attempt to

match visible corporate partner logos on the vehicles with alliances compiled in the data

(Donaldson, 2002; Schlegelmilch & Lehbrink, 2004). These cross-verification efforts supported

the general reliability of the ChicaneF1 team-sponsor data and served to clear up ambiguities

where present. To compile historical team performance data, we referenced the official F1

website (Formula One Administration Ltd., n.d.). The resulting dataset consisted of 124 separate

F1 team enterprises2, 776 team years3, 1,077 sponsoring firms, and 5,054 team-sponsor alliance

years.

1 Prior to 1967, corporate sponsorship was prohibited by F1 regulations; thus sponsorship data begins in 1967. 2 In addressing the sale of teams (represented by name changes) within the collection of 124 team enterprises, we

recognized the organization as continuing to exist as a consistent enterprise when the name changes. This data

treatment is consistent with the fact that corporate sponsorships, team personnel, and even previous season team

results (for the purposes of grid and pit positioning) are typically transferred as a condition of the sale. 3 Of the total 776 team years, 570 occurred after 1967 when the institutional regulations first permitted corporate

sponsorship alliances. This latter timeframe serves as the context of the investigation here, though drivers’

championships and team experience accumulated prior to 1967 are represented in the analyzed data.





Dependent Variable

The event history method analyzes event occurrences or changes in a subject or

organization’s condition over time. Event history modeling has gained prominence in

organizational research in recent years due to the method’s capacity for analyzing data that is

both cross-sectional and longitudinal in nature (e.g., Iyer & Miller, 2008; Lange et al., 2009; Yu

& Cannella, 2007). Here, the dataset is longitudinal in annual intervals and cross-sectional in

that a multitude of teams are chronicled. Analogous to patient survival in clinical research

(Willett & Singer, 1993), the potential outcome conditions in any given time period of the F1

teams studied here are only two-fold: operational (alive) or nonoperational (dead). While

theoretically survival may be more difficult to discern when applied to organizations (Freeman et

al., 1983), the motor racing context enables delineation based on whether a team competed in a

race in a particular season or not. Therefore, we compose a binary survival term coded (1) if a

team dissolved in a given year (i.e., did not compete further following that season) and (0) if the

team continued in competition following the given year.

From this data, we generate annual hazard rates based on the proportion of the teams at

risk with a given level of experience that incur dissolution (Willett & Singer, 1993). This hazard

of team dissolution represents the dependent variable in survival analysis (Audretsch &

Mahmood, 1995). Essentially, the event history method produces an age-based hazard function

for the sample of enterprises that represents the chronological probability of team failure, given

the team has not yet dissolved. The hazard function accounts for both censored and non-

censored cases in computing probabilities, which is vital to the analysis given that enterprises

still in existence have yet to experience dissolution and are therefore right-censored in the

dataset. The independent variables then predict probabilities of dissolution, or hazard rates. As a





result, a negative coefficient to an independent variable signifies a reduced hazard of dissolution,

or similarly, an increased probability of survival.

Independent Variables

The first three hypotheses addressed the nature of resources exchanged between alliance

partners according to the complementarity between the sponsoring firm’s industry4 and the

team’s competitive environment (Sarkar, Echambadi, Cavusgil et al., 2001). Prior to classifying

the alliances in this context, two of the authors developed alliance resource designations of (1)

performance-based, (2) financial, or (3) operational by studying the relevant literature referenced

in the hypotheses outlined above and extensively reviewing the press announcements of over one

hundred F1 alliances. The third author and two F1 team alliance managers then reviewed these

three designations and confirmed their face validity. We defined performance-based alliance

resources as those directly contributing to the team’s racing performance on the track. Financial

alliance resources were monetary in nature. Operational alliance resources contributed to the

organizational operations of the team but not team performance in racing competition. Next, two

of the authors independently classified each alliance in the data set as primarily performance-

based, financial, or operational. The initial inter-coder reliability was 89 percent, and conflicts

were subsequently reconciled through discussion and further clarification of the classification

descriptions, as well as a review of alliance announcements within the relevant industry under

evaluation. Upon completion of the alliance classifications, we compiled three variables

measuring the count of performance, financial, and operational alliances for each F1 team in

each year of the data set.

4 Given the historical nature of the dataset, a primary industry classification for the sponsoring firm was only

feasible for 91.75 percent of the team-sponsor alliance years in the raw dataset. For example, even after consulting

various sources, we could make no solid determination as to what sponsoring firm was referenced by “LBT” in

relationship to the 1982 March racing team. As a result, the analysis and data descriptions are inclusive of solely the

sponsoring firm data with verifiable industry designations.





To test the fourth hypothesis, which predicted diminishing returns to alliance resources, a

separate quadratic term for each of the three alliance category variables was formulated (Hoang

& Rothaermel, 2005). If such an effect existed, we would expect to see a positive coefficient on

the quadratic terms, signifying that as the number of alliances offering the specified type of

resource reached a certain level, incremental alliances of that type would positively influence the

odds of enterprise dissolution.

Concerning the fifth hypothesis, we summed the years of F1 involvement for all

sponsoring firms in a team’s alliance network to represent the alliance partners’ experience in the

competitive context. Therefore, if a team was engaged in five corporate alliances, and each of its

five sponsoring firms had been involved in F1 for the previous ten years (whether with that team

or other teams), the team would claim 50 years of sponsoring firm experience in its alliance

network.

Control Variables

The ability of an enterprise to compete for scarce resources versus rivals in its

environment is a basic test of survival in organizational ecology (Hannan & Freeman, 1977).

Competition may exist for sales, alliance formations, certifications, ratings, distribution, or even

in head-to-head product tests. For both new and established ventures, performance in such

competitions weighs heavily in the process of legitimization, the formation of status hierarchies,

and the building of reputation (Rao, 1994). In this way, performance may affect access to

resources both directly and indirectly. Superior sales performance, ratings, and achievement in

head-to-head competitions directly generate certain financial and organizational resources.

Additionally, ceteris paribus, potential interorganizational alliance partners theoretically desire to

align with prestigious others (Stuart, 1998), indicating that generating prestige through superior





performance might create additional alliance opportunities to access even further resources. In

these ways, enterprise performance seems highly probable to influence survival.

In this study, we account for the performance of F1 teams both recently and historically.

We operationalize recent success through a rolling average of the annual points earned by a team

over the previous five years5. Historical success we derive by an aggregation of the drivers’

championships won by a team up prior to a given season. Finally, we include both the number of

times in which a team has been sold prior to a given year and the world GDP annual growth rate

(USDA, 2009) as additional covariates relating to the characteristics of a team and the global

macroeconomic condition, respectively.

Descriptive statistics and the correlation matrix for the variables appear in Table 1.

Unsurprisingly given the large sample size, almost all correlations are statistically significant.

However, all variance inflation factors (VIF) are less than ten, which indicates multi-collinearity

is not particularly concerning (Hair, Anderson, Tatham, & Black, 1995; Marquardt, 1970;

O’Brien, 2007)6.

Model Estimation

In this study, we utilized Cox proportional hazards regression to model the data (Cox,

1972). As we mentioned above, the hazard function plays a critical role in modeling event

history data and is based on the chronological shape of the hazard rate. Parametric models

5 Formula One Administration (FOA) awards points to teams at the conclusion of each race based on the finish of

their cars in that race. More points are awarded for better finishes. FOA then maintains a running tally of points

throughout the season, which is deemed the Constructors’ Championship. FOA also tracks individual drivers’

earned points throughout the race season for the awarding of the Drivers’ Championship, which has traditionally

been the more prestigious of the F1 Championship awards. 6 VIF statistics ranged from 1.074 to 7.349 (Drivers’ Championships). Even though VIF values less than 10 indicate

inconsequential collinearity (Hair et al., 1995; O’Brien, 2007), several alternate models were analyzed that isolated,

excluded, and transformed variables that were correlated above 0.70 (Van den Poel & Lariviere, 2004). Coefficient

values and model significance did not change substantially between models compared to the primary specification.

Therefore, we judged estimates within the primary model to be generally robust to collinearity concerns.

Insert Table 1 about here





assume a specified shape of the hazard function and the effect of covariates, while nonparametric

models make no such assumptions. However, nonparametric models are very limited in their

ability to consider multiple groups—specified by the covariates—and lack multivariate controls

(Audretsch & Mahmood, 1995). Cox regression is semi-parametric and therefore makes no

assumptions of the shape of a baseline hazard function, which is the probability that dissolution

will occur after any given duration. Although hazard rates are assumed to be proportional

between groups in semi-parametric models7, this technique is preferred because of its ability to

model both time dependent and continuous covariates, such as an F1 team’s points scored.

Across multiple seasons, a team will score different quantities of points based on race results.

Therefore, the magnitude of the points variable will vary by season (time). Similarly, the

variables indicating a team’s compilation of performance, financial, and operational sponsors, as

well as cumulative drivers’ championships will vary from season to season. By utilizing a

counter variable in units of time, Cox regression assumes the rate of dissolution increases with

time, depending on the model’s independent variables (Tsai, 2000).

RESULTS

In this study, we propose an empirical model that predicts the dissolution of enterprises

reliant on alliance-based resources for survival. To test our theoretical hypotheses, we start with

a model inclusive of control variables and the primary variables representing three categories of

alliance-based resources—performance, financial, and operational (Model 1 in Table 2). Then,

we insert quadratic terms to examine the possibility of diminishing returns to each resource

category (Model 2). Both models outperform a constant-only model at a highly significant level

(p < .001). The second model also outperforms nested Model 1 (p < .05). The models’

7 This proportionality assumption is relaxed when any independent variable is interacted with time, such as the

analysis of eras (i.e., the pre-1996 variable in this study).





estimated parameters and applicable statistics are displayed in Table 2 and further discussed

below in relation to the study’s hypotheses.

Hypothesis One (H1) was the first of three hypotheses that examined the effects of

sponsorship alliances, which offer different types of resources to the competing teams. As

generally predicted in H1 and H2, access to both performance and financial resources

demonstrated at least marginally significant negative influences on enterprise dissolution in both

models. For each additional performance-based partner with which a team aligned, the team’s

odds of dissolution the following season reduced by 56 percent (or a factor of 0.44)8. In line

with the expectation that financial sponsors would be less influential on team dissolution than

performance-based sponsors, financial sponsors had slightly less impact on team survival—

reducing the odds of dissolution by 48 percent for each additional financial sponsor. Further, we

hypothesized that based on organizational learning theory, the effect of performance-based

resources would weaken as the enterprise gained experience (H1b). Indeed, the base model

empirically supported this interaction with team experience (p < .05). As a team gains a year of

experience, the marginal contribution of an additional performance-based sponsor toward

dispelling the hazard of dissolution is reduced by 3.0 percent. However, this effect was nullified

by the inclusion of quadratic terms in the full model—most likely due to correlation between

team experience and greater numbers of performance-based sponsors. We found no significant

support for the prediction (H3) that access to operational resources reduced the hazard of team

dissolution.

8 In the Cox regression model, the anti-log of the variable’s coefficient produces the hazard ratio, which is the

dissolution rate for an enterprise with one more unit of the variable in comparison to the dissolution rate for another

enterprise without that additional variable unit. As it concerns performance-based sponsors, the anti-log of the

estimated coefficient (e^-0.81) produces a ratio of 0.44, which indicates that a one unit increase in performance-

based alliances yields a 56% reduction in the odds of the team dissolving in the following season.

Insert Table 2 about here





Given that sponsorships offering either performance or financial resources are deterrents

to the hazard of team dissolution, we offer an interesting test of diminishing returns to these

effects in the fourth hypothesis. We examine this hypothesis in a second model (Model 2) that

includes the associated quadratic terms in addition to the linear terms for each resource category,

team and sponsors’ experience, and the control variables. This model specification maintained

the main effects of the number of performance and financial sponsors as negative influencers of

the odds of dissolution while also revealing significant positive quadratic terms for both resource

types (though the squared-financial resources variable only reached marginal statistical

significance [p < .10]). The positive coefficients on the quadratic terms signify diminishing

returns to both resource types. That is, as the number of sponsor alliances offering performance

or financial resources reaches a certain level, incremental sponsorships of each type positively

influence the odds of team dissolution. Although these results offer support for H4a and H4b,

neither the main effect nor quadratic term for operational alliances (H4c) was significant.

The fifth hypothesis (H5) predicted that the aggregated experience of a team’s corporate

alliance network in the competitive context of F1 motor racing would be negatively related to the

dissolution of the team. The analysis did not substantiate this effect. Although significant in the

hypothesized direction in a model isolating experience without any other predictors (β = -.027, p

< .01), any apparent effect of the aligned sponsors’ experience was seemingly nullified by other

correlated variables in more comprehensive models. The use of alternative measurements of the

experience within a team’s corporate alliance network and transformations of correlated

variables show similar patterns where sponsors’ experience is a significant deterrent of team

dissolution in isolation but not in more substantial models.





Of the control variables, only the term measuring recent team performance reached

statistical significance. In the five-year average of team points, each additional point reduced a

team’s odds of dissolution by 7.7 percent. None of the additional control variables—which

quantify historical performance (i.e., drivers’ championships), sales of a team, and the annual

global GDP growth—achieve significance in either model specification.

In summary, the broad findings across hypotheses indicate that sponsorship alliances

offering access to certain resources contribute to the survival of sponsored teams even when

controlling for teams’ competitive performance. Specifically, alliances that offer an either

performance or financial resources exhibit a significant negative relationship to team dissolution.

We also uncover evidence that suggests the relationship to team dissolution may be subject to

diminishing returns.

DISCUSSION

In this study, our primary research question asks how access to various resources through

interorganizational alliances influences the survival of enterprises in a highly competitive

environment. To address this question, we studied teams competing in F1 motor racing over the

previous 40 years as enterprises that rely on their alliances with sponsoring firms to access

different types of organizational resources. Though a team may offer a consistent resource (i.e.,

promotional services) within its network of sponsorship alliances, the reciprocal resources

accessed are not necessarily equivalent and their impact on team outcomes may differ.

By accessing either performance or financial resources through sponsors, teams in the F1

context were able to reduce their odds of dissolution by over 65% in this study. However,

alliances offering operational resources had no effect on the team’s propensity to survive.

Though contrary to the third hypothesis, this finding did confirm the anticipated lower priority of





operational resources. Such divergent effects based on resource type supports Grant’s (1991)

theory of heterogeneous resources and challenges organizational scholars to examine alliance

contributions closely. Future researchers should be mindful of the utility of alliance

categorization based on the exchanged resources’ strategic application in the relevant

institutional environment. Segmenting alliance resources in this fashion follows organizational

ecology theory, which emphasizes the roles of both competition and environmental selection in

enterprise survival (Hannan & Freeman, 1977; Ulrich & Barney, 1984). This contribution

supports Skilton’s claim that dividing resources as knowledge-based or property-based is too

broad and should instead be “supplemented by an understanding of the functions of different

resources in a production system” (Skilton, 2009, p. 840).

While this study’s findings substantiate that sponsorships offering access to performance-

based resources are effective deterrents to team dissolution in this context, the survival impact of

such sponsorships appears to weaken as teams gain experience. Theoretically, this interaction

effect between alliance-based performance resources and organizational experience arises

because F1 teams as entrepreneurial enterprises become less reliant on external sources for a

competitive performance advantage as teams develop internal performance competencies over

time. As a maturing organization internalizes the know-how of competition within its

environment, the organization can become increasingly self-reliant in regards to performance

expertise (Levitt & March, 1988). Therefore, a new or young team would be wise to prioritize

sponsorship alliances with firms offering resources directly related to competitive performance.

Once teams establish performance expertise internally, teams may wish to reallocate their focus

to more financially oriented sponsorships while being cognizant of the potential for diminishing

returns.





The influence of the quadratic terms in this investigation provides support for the

argument that alliances offering access to performance or financial resources contribute to

entrepreneurial survival but are not unlimited in their capacity to ward off dissolution. At a

certain threshold, adding incremental sponsorships fails to discourage enterprise dissolution,

thereby suggesting an inverted U-shape relationship. This finding further substantiates the theory

that organizations possess an alliance management capability (Ireland et al., 2002), which is not

without restrictions. Though Deeds and Hill (1996) uncovered a similar effect when examining

the influence of alliances on rates of new product development, the idea of diminishing returns to

alliance engagement has yet to be widely adopted in interorganizational research (Rothaermel &

Deeds, 2006). At the very least, studies quantifying alliance propensity as related to enterprise

performance should consider curvilinear possibilities. Yet, future researchers should recall this

potential for diminishing returns in light of the study’s limitations. Although we quantify the

number of alliances offering access to different resources, the magnitude of the resources

exchanged within each alliance is not known. Therefore, a wide disparity in the magnitude of

resources exchanged within each alliance is possible. Where such a disparity exists, a team

could access resources worth US$50 million annually from just one alliance, while other teams

may be dependent on five alliances providing US$10 million each to access equivalent resources.

Still, the evidence offered in this study for diminishing returns, in combination with the varying

influence on survival of different sponsorship resources, represent relevant implications for

ongoing research in this domain.

By analyzing over four decades of interorganizational alliances between F1 teams and

their corporate partners, this study has taken the perspective of entrepreneurial enterprises that

engage in alliances by offering promotional services to sponsoring firms in exchange for various





other resources. The relationship between this exchange process and the team’s propensity to

survive was explicated, and certain resources were identified as more crucial than others. For

practitioners, the results offer empirical evidence to consider in the prioritization of alliance

resources. For scholars, the findings advance the broadening scope of the resource-based view in

marketing (Auh & Benguc, 2009), where the strategic value of organizational resources is a

function of both internal and competitive influences.


APA cite: Cobbs, J. B., Tyler, B. D., & Chan, C. K. (2012, June). Survival via sponsorship alliances: Not all exchanged resources are equal. Research presented

at the Warsaw Sport Marketing Center’s Research Conference in Sport Marketing: Focus on Sponsorship, Portland, OR.

TABLE 1

Descriptive Statistics and Correlationsa

Variables Mean s.d. 1 2 3 4 5 6 7 8

1. Performance-based sponsors 4.46 5.47

2. Team experience (yrs.) 11.88 12.12 .46

3. Financial sponsors 2.14 2.80 .73 .35

4. Operational sponsors 1.53 2.78 .77 .33 .74

5. Team Sponsors’ experience (yrs.) 40.54 52.71 .85 .65 .64 .67

6. Team points 25.46 35.37 .42 .69 .23 .23 .65

7. Drivers' Championships 1.87 3.10 .33 .86 .14 .14 .57 .81

8. Team sold 0.04 0.19 .13 .01 .18 .16 .07 -.09 -.10

9. Global GDP growth (%) 3.11 1.29 -.03 -.07 -.02 -.01 -.05 -.00 -.03 -.03

a There are 570 team years and 124 unique teams. Correlations with absolute values equal or greater than .09 are significant at the p < .05 level.


APA cite: Cobbs, J. B., Tyler, B. D., & Chan, C. K. (2012, June). Survival via sponsorship alliances: Not all exchanged

resources are equal. Research presented at the Warsaw Sport Marketing Center’s Research Conference in Sport

Marketing: Focus on Sponsorship, Portland, OR.

TABLE 2

Results of Event History Analysis of Team Dissolution

Variables

Hypothesis

(effect) Model 1 Model 2

Performance-based sponsors 1 (-) -0.64 *** (0.17) -0.81 *** (0.18)

Performance sponsors squared 4a (+)

0.03 *** (0.01)

Team experience

0.09

(0.09) 0.11

(0.10)

Performance sponsors × Team

experience 1b (+) 0.03 ** (0.01) 0.01

(0.01)

Financial sponsors 2 (-) -0.28 ̂ (0.15) -0.65 ** (0.25)

Financial sponsors squared 4b (+)

0.07 ̂ (0.03)

Operational sponsors 3 (-) 0.14

(0.19) 0.15

(0.29)

Operational sponsors squared 4c (+)

-0.02

(0.03)

Team sponsors' experience 5 (-) 0.02

(0.02) 0.03

(0.02)

Team points control (-) -0.09 ** (0.03) -0.08 ** (0.03)

Drivers' championships control (-) -0.38

(0.33) -0.42

(0.40)

Team sold control -16.12

(1489) -24.18

(1040)

Global GDP growth control -0.02

(0.11) -0.03

(0.11)

- 2 log-likelihood

241.83

232.85

Likelihood ratio χ² (df)

73.76 *** (10)

82.74 *** (13)

Change vs. prior nested model

(1, 4) 8.975 *

^ p < .10

* p < .05

** p < .01

*** p < .001

Two-tailed tests. Standard errors are in parentheses.





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